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We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Psychedelics have sparked widespread interest as they offer a potential new form of therapeutic treatment. This thesis paper explores the current and upcoming psychedelics that are being researched for their use in a therapeutic setting. The main substances discussed are lysergic acid diethylamide (LSD), methylenedioxymethamphetamine (ecstasy/molly/MDMA), and ketamine (esketamine). This

Psychedelics have sparked widespread interest as they offer a potential new form of therapeutic treatment. This thesis paper explores the current and upcoming psychedelics that are being researched for their use in a therapeutic setting. The main substances discussed are lysergic acid diethylamide (LSD), methylenedioxymethamphetamine (ecstasy/molly/MDMA), and ketamine (esketamine). This paper also discusses the mechanism of action for each drug and the underlying research that has been found to support the ethical use of these substances alongside talk therapy

ContributorsPiemont, Sabrina (Author) / Jimenez Arista, Laura (Thesis director) / Ocampo Hoogasian, Rachel (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2023-05